SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8176–8200 of 15113 papers

TitleStatusHype
Neurosymbolic Reinforcement Learning and Planning: A Survey—0
Neuro-Symbolic Reinforcement Learning with First-Order Logic—0
Neuro-Symbolic World Models for Adapting to Open World Novelty—0
NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming—0
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning—0
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
New Reinforcement Learning Using a Chaotic Neural Network for Emergence of "Thinking" - "Exploration" Grows into "Thinking" through Learning -—0
News-based trading strategies—0
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs—0
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models—0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Node Injection Attacks on Graphs via Reinforcement Learning—0
Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks—0
Noise-based reward-modulated learning—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Some approaches used to overcome overestimation in Deep Reinforcement Learning algorithms—0
Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning—0
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks—0
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events—0
Noisy Spiking Actor Network for Exploration—0
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL—0
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme—0
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling—0
Show:102550
← PrevPage 328 of 605Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified